Unpacking Micro Data Videos: Key Elements and Design Practices in Minute-Long Data Videos for Mobile Usage MHCI036
Bibliographic record
Abstract
Micro Data Videos (mDVs) are up to one-minute data-driven vertical video clips for mobile devices. Widely adopted on social media platforms, mDVs hold significant potential for disseminating information. Despite their growing prevalence, little is known about their components and how they are designed. Thus, two studies were conducted. Study 1 analyzed 40 mDVs and revealed their narrative components. Study 2 examined, through design sessions with design experts, how such components are assembled to craft storyboards for mDVs. The diverse narrative styles of mDVs render them flexible and suitable for multiple topics and purposes. Further, many include a “Linker" directing viewers to external online resources. Participants approached their design in a structural yet iterative manner with emphasis on setting up a “hook” in the opening seconds to capture attention. We summarize and share common design practices used in creating mDVs, an increasingly important medium in data storytelling.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".